April 22, 2015
ggplot2 is an R package by Hadley Wickham that lets you make beautiful R graphics (relatively) easily.
It's part of the Hadleyverse, which I recommend everyone get to know (dplyr, reshape2, stringr, lubridate… and many more).
The name ggplot2 refers to a famous book on data visualization theory called The Grammar of Graphics.
First, we need to install and load the package,
#install.packages("ggplot2")
library(ggplot2)
To start, I'm going to use the diamonds data that comes with ggplot2,
str(diamonds)
## 'data.frame': 53940 obs. of 10 variables: ## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ... ## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ... ## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ... ## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ... ## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ... ## $ table : num 55 61 65 58 58 57 57 55 61 61 ... ## $ price : int 326 326 327 334 335 336 336 337 337 338 ... ## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ... ## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ... ## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
qplot(carat, data=diamonds)
qplot(carat, data=diamonds)
qplot() performs similar functionality to the base R graphics function plot(). But it aleady might seem a little different, because we're not using the $ operator.
Instead, you're listing the name of the variable(s) and then telling R where to "look" for that variable with "data=". This is like what we do when modeling using functions like lm().
qplot(clarity, fill=cut, data=diamonds)
But, in order to really harness the power of ggplot2 you need to use the more general ggplot() command. The idea of the package is you can "layer" pieces on top of a plot to build it up over time.
You always need to use a ggplot() call to initialize the plot. I usually put my dataset in here, and at least some of my "aesthetics." But, one of the things that can make ggplot2 tough to understand is that there are no hard and fast rules.
p1 <- ggplot(aes(x=clarity, fill=cut), data=diamonds)
If you try to show p1 at this point, you will get "Error: No layers in plot." This is because we haven't given it any geometric objects yet.
In order to get a plot to work, you need to use "geoms" (geometric objects) to specify the way you want your variables mapped to graphical parameters.
p1 + geom_bar()
p1 + geom_bar(position="dodge")
p1 + geom_bar(position="fill")
p2 <- ggplot(aes(x=carat, y=depth), data=diamonds) p2 + geom_point()
p2 + geom_bin2d()
Notice that I'm not saving these geom layers– I'm just running
p2 + [something]
to see what happens. But, I can save the new version to start building up my plot,
p2 <- p2 + geom_bin2d()
p2 <- p2 + xlab("Carat") + ylab("Depth") +
guides(fill=guide_legend(title="Number of diamonds"))
p2
p2 + scale_fill_continuous(breaks=c(1500, 2500, 3500,4500))
p2 + scale_fill_continuous(trans="log")
p2 + scale_fill_continuous(trans="log", breaks=c(1,10,100,1000,5000))
p2 + facet_wrap(~color)
p2 + facet_grid(color~clarity)
# install.packages("lubridate")
# install.packages("dplyr")
library(lubridate)
library(dplyr)
econ <- economics str(econ)
## 'data.frame': 478 obs. of 6 variables: ## $ date : Date, format: "1967-06-30" "1967-07-31" ... ## $ pce : num 508 511 517 513 518 ... ## $ pop : int 198712 198911 199113 199311 199498 199657 199808 199920 200056 200208 ... ## $ psavert : num 9.8 9.8 9 9.8 9.7 9.4 9 9.5 8.9 9.6 ... ## $ uempmed : num 4.5 4.7 4.6 4.9 4.7 4.8 5.1 4.5 4.1 4.6 ... ## $ unemploy: int 2944 2945 2958 3143 3066 3018 2878 3001 2877 2709 ...
econ$date <- ymd(econ$date) old <- filter(econ, year(date)<"1970") old$date <- ymd(old$date)
p3 <- ggplot(data=old) + geom_line(aes(x=date, y=pce)) p3
# install.packages("scales")
library(scales)
p3 +
scale_x_datetime(breaks = date_breaks("6 months"),
labels = date_format("%b %Y"))
p3 +
scale_x_datetime(breaks = date_breaks("6 months"),
labels = date_format("%B %y"))
#install.packages("ggmap")
library(ggmap)
str(crime) # crime data from ggmap package
## 'data.frame': 86314 obs. of 17 variables: ## $ time : POSIXt, format: "2009-12-31 22:00:00" "2009-12-31 22:00:00" ... ## $ date : chr "1/1/2010" "1/1/2010" "1/1/2010" "1/1/2010" ... ## $ hour : int 0 0 0 0 0 0 0 0 0 0 ... ## $ premise : chr "18A" "13R" "20R" "20R" ... ## $ offense : Factor w/ 7 levels "aggravated assault",..: 4 6 1 1 1 3 3 3 3 3 ... ## $ beat : chr "15E30" "13D10" "16E20" "2A30" ... ## $ block : chr "9600-9699" "4700-4799" "5000-5099" "1000-1099" ... ## $ street : chr "marlive" "telephone" "wickview" "ashland" ... ## $ type : chr "ln" "rd" "ln" "st" ... ## $ suffix : chr "-" "-" "-" "-" ... ## $ number : int 1 1 1 1 1 1 1 1 1 1 ... ## $ month : Ord.factor w/ 8 levels "january"<"february"<..: 1 1 1 1 1 1 1 1 1 1 ... ## $ day : Ord.factor w/ 7 levels "monday"<"tuesday"<..: 5 5 5 5 5 5 5 5 5 5 ... ## $ location: chr "apartment parking lot" "road / street / sidewalk" "residence / house" "residence / house" ... ## $ address : chr "9650 marlive ln" "4750 telephone rd" "5050 wickview ln" "1050 ashland st" ... ## $ lon : num -95.4 -95.3 -95.5 -95.4 -95.4 ... ## $ lat : num 29.7 29.7 29.6 29.8 29.7 ...
midlat <- mean(crime$lat, na.rm=TRUE)
midlon <- mean(crime$lon, na.rm=TRUE)
m1 <- get_map(source='stamen', location=c(midlon, midlat),
maptype="watercolor", zoom=11)
p4 <- ggmap(m1)
p4
p4 + geom_point(aes(x=lon, y=lat), data=crime)
p4 + geom_hex(aes(x=lon, y=lat), data=crime, alpha=0.8) +
guides(fill=guide_legend(alpha=0.5, title="Number of crimes")) +
xlab("") + ylab("") + theme(axis.line=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank())
baseplot <- ggplot() +
aes(x=citystate, y=Freq, fill = Response, order=Response) +
facet_wrap(~year, nrow=3)+geom_bar(data = trial2$neg, stat = "identity") +
scale_fill_manual(breaks=c("Not at all satisfied", "2", "3", "4",
"Extremely satisfied"), values=colorsB,
name="Response") +
geom_bar(data = trial2$pos, stat = "identity") + coord_flip() +
ggtitle("Community satisfaction") + xlab("") +ylab("") +
scale_y_continuous(limits=c(-0.5, 1),
breaks=seq(from=-0.5, to=0.75, by=0.25),
labels=c("50%", "25%", "0", "25%", "50%", "75%")) +
theme(legend.text=element_text(size=14),
legend.title=element_text(size=16),
axis.text=element_text(size=14),
strip.text=element_text(size=14))
baseplot
# install.packages("RColorBrewer")
library(RColorBrewer)
display.brewer.all(n=5, type="qual")
p1 + geom_bar(position="dodge") + scale_fill_brewer(palette="Dark2")
ggsave("beautiful_hex_map.jpg", plot=p4)